New mathematical tools help understand cell functions, adaptability, and evolvability to discover hidden variables to predict phenotypes that could be tested in the future in wet labs. Different models have been successfully used to discover the properties of the protein-protein interaction networks or interactomes. I found that in the hyperbolic Popularity-Similarity model, cellular proteins with the highest contents of structural intrinsic disorder cluster together in many different eukaryotic interactomes and this is not the case for the prokaryotic E. coli, where proteins with high degree of intrinsic disorder are scarce. I also found that the normalized theta variable from the Popularity-Similarity model for orthologues proteins correlate to the complexity of the organisms in analysis.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.biosystems.2024.105351 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Intrinsic structural disorder on proteins is involved in the interactome evolution.
Biosystems
December 2024
Instituto de Histología y Embriología (IHEM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Cuyo (UNCuyo), 5500, Mendoza, Argentina; Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Cuyo (UNCuyo), Mendoza, Argentina. Electronic address:
New mathematical tools help understand cell functions, adaptability, and evolvability to discover hidden variables to predict phenotypes that could be tested in the future in wet labs. Different models have been successfully used to discover the properties of the protein-protein interaction networks or interactomes. I found that in the hyperbolic Popularity-Similarity model, cellular proteins with the highest contents of structural intrinsic disorder cluster together in many different eukaryotic interactomes and this is not the case for the prokaryotic E.
View Article and Find Full Text PDFGeneralised popularity-similarity optimisation model for growing hyperbolic networks beyond two dimensions.
Sci Rep
January 2022
Department of Biological Physics, Eötvös Loránd University, Pázmány P. stny. 1/A, 1117, Budapest, Hungary.
Hyperbolic network models have gained considerable attention in recent years, mainly due to their capability of explaining many peculiar features of real-world networks. One of the most widely known models of this type is the popularity-similarity optimisation (PSO) model, working in the native disk representation of the two-dimensional hyperbolic space and generating networks with small-world property, scale-free degree distribution, high clustering and strong community structure at the same time. With the motivation of better understanding hyperbolic random graphs, we hereby introduce the dPSO model, a generalisation of the PSO model to any arbitrary integer dimension [Formula: see text].
View Article and Find Full Text PDFThe inherent community structure of hyperbolic networks.
Sci Rep
August 2021
Department of Biological Physics, Eötvös Loránd University, Pázmány P. stny. 1/A, Budapest, 1117, Hungary.
A remarkable approach for grasping the relevant statistical features of real networks with the help of random graphs is offered by hyperbolic models, centred around the idea of placing nodes in a low-dimensional hyperbolic space, and connecting node pairs with a probability depending on the hyperbolic distance. It is widely appreciated that these models can generate random graphs that are small-world, highly clustered and scale-free at the same time; thus, reproducing the most fundamental common features of real networks. In the present work, we focus on a less well-known property of the popularity-similarity optimisation model and the [Formula: see text] model from this model family, namely that the networks generated by these approaches also contain communities for a wide range of the parameters, which was certainly not an intention at the design of the models.
View Article and Find Full Text PDFOptimisation of the coalescent hyperbolic embedding of complex networks.
Sci Rep
April 2021
Department of Biological Physics, Eötvös Loránd University, Pázmány P. stny. 1/A, Budapest, 1117, Hungary.
Several observations indicate the existence of a latent hyperbolic space behind real networks that makes their structure very intuitive in the sense that the probability for a connection is decreasing with the hyperbolic distance between the nodes. A remarkable network model generating random graphs along this line is the popularity-similarity optimisation (PSO) model, offering a scale-free degree distribution, high clustering and the small-world property at the same time. These results provide a strong motivation for the development of hyperbolic embedding algorithms, that tackle the problem of finding the optimal hyperbolic coordinates of the nodes based on the network structure.
View Article and Find Full Text PDFTopological motifs populate complex networks through grouped attachment.
Sci Rep
August 2018
Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea.
Network motifs are topological subgraph patterns that recur with statistical significance in a network. Network motifs have been widely utilized to represent important topological features for analyzing the functional properties of complex networks. While recent studies have shown the importance of network motifs, existing network models are not capable of reproducing real-world topological properties of network motifs, such as the frequency of network motifs and relative graphlet frequency distances.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!